Development and internal validation of a nomogram to predict postoperative atrial fibrillation in elderly patients with lung cancer

Introduction

Atrial fibrillation is a common supraventricular arrhythmia. Its average prevalence in the general population is approximately 1.6% (1), and it continues to increase with age (2). In developed countries, reports indicate that the incidence of atrial fibrillation among citizens aged 80 and above is no less than 9% (3). This statistic vividly illustrates the pronounced tendency of atrial fibrillation to target the elderly population. Atrial fibrillation is an independent risk factor for ischemic stroke (4). It leads to a fivefold elevation in the risk of ischemic stroke and a twofold increase in mortality (5). It is also associated with vascular dementia and thromboembolism in other organs, affecting people’s quality of life (6).

Postoperative atrial fibrillation (POAF), the most common secondary atrial fibrillation (7), refers to the development of new-onset atrial fibrillation following surgery in patients who have no previous history of being diagnosed with atrial fibrillation (8). It not only increases the length of hospital stay and costs but also leads to the occurrence of various adverse cardiovascular events (9,10). POAF occurs not only after cardiac surgery (11), but is also highly prevalent after lung cancer surgery. Previous studies have shown that the incidence of atrial fibrillation after lung cancer surgery ranges from 4% to 20% (12). It can lead to an extended average hospital stay, an increased economic burden on patients, and may even trigger serious postoperative complications such as cerebral infarction, increasing the in-hospital mortality rate (13,14).

The main group of people affected by lung cancer is the elderly. A study has shown that the median age at diagnosis of lung cancer is 71 years (15). Currently, thoracoscopic lung resection is the primary treatment method for stage I, stage II, and some stage IIIA non-small cell lung cancers (NSCLC) (16). Compared with younger patients, elderly patients have poor physiological functions. They are at a higher risk of developing POAF after lung resection, which is more likely to trigger serious complications such as heart failure, myocardial infarction, pulmonary embolism, and ischemic stroke, seriously affecting the recovery and prognosis of patients (14).

The pathogenesis of POAF is closely related to many factors, such as the type of surgery, underlying diseases, and age (17). Hence, identifying the high-risk factors for POAF and predicting high-risk populations are of great significance for the management of POAF. Currently, research on POAF mainly focuses on two aspects. One aspect is centered around the statistics of the incidence of POAF and the exploration of treatment strategies. The other aspect revolves around the analysis of influencing factors of POAF after various cardiac surgeries. Even though some researchers explore POAF after non-cardiac surgery, there is still a gap in research specifically targeting elderly patients who develop POAF after lung cancer surgery. Focusing on postoperative complications in thoracic oncology, we identified modifiable POAF predictors and established a bedside-usable nomogram validated by bootstrap resampling. We present this article in accordance with the TRIPOD reporting checklist (18) (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-533/rc).

Methods

Study design

This study was a single-center, retrospective observational study. The research subjects were patients who underwent lung cancer surgery in the Department of Cardiothoracic Surgery of Zhoushan Hospital in Zhejiang, China, from January 2022 to December 2023. Based on national geriatric criteria (aged ≥60 years in China), inclusion criteria comprised: (I) aged ≥60 years; (II) underwent video-assisted thoracic surgery (VATS) performed by surgeons with >5-year experience; (III) pathologically diagnosed with lung cancer. Exclusion criteria were: (I) cases with missing or incomplete data; (II) cases that underwent other types of surgeries simultaneously during lung cancer surgery, such as combined chest wall tumor and lung cancer surgery; (III) preexisting paroxysmal or persistent atrial fibrillation. After excluding 23 ineligible cases, 960 consecutive patients aged ≥60 years who underwent VATS for NSCLC were included for model construction and internal validation (Figure 1). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Zhoushan Hospital (No. 2022-155). and the requirement to obtain informed consent was waived.

Figure 1 Flow chart of patient selection and statistical analysis. AUC, area under the curve; DCA, decision curve analysis; HL test, Hosmer-Lemeshow goodness-of-fit test; LASSO, least absolute shrinkage and selection operator; ROC curve, receiver operator characteristic curve.

Variables

Through the electronic medical record system, researchers reviewed the medical records, prescription information, and various examinations and test reports during hospitalization, and collected the following types of data: (I) basic patient information including age, gender, educational level; (II) living habits including whether smoking or not, whether drinking alcohol or not; (III) medical history including type 2 diabetes, hypertension, non-hypertensive cardiovascular diseases, respiratory illnesses, urological disorders, digestive disorders, neurological disorders, malignancy, history of surgery, history of pulmonary resection; (IV) surgery-related information including number of surgically resected sites, whether lobectomy was performed, lymph node dissection and intraoperative blood loss; (V) postoperative conditions including whether anti-infection treatment was received, more than 3 times of postoperative rescue analgesia, number of days of indwelling of thoracic drainage tube; (VI) test indicators including electrolyte imbalance on the day of surgery, the first day after surgery, and the third day after surgery, as well as a critical value for hypokalemia within 72 hours of surgery.

In this study, anti-infection treatment refers to the application of antibiotics when there is a confirmed infection. The use of antibiotics from the preoperative period up to 24 hours postoperatively is regarded as perioperative prophylactic use of antibiotics and does not belong to antibiotic therapy for infection control (19). The determination of electrolyte imbalance is based on the results of blood biochemical tests, with a focus on whether the levels of common electrolytes such as serum sodium, serum potassium, and serum chloride deviate from the normal ranges. Non-hypertensive cardiovascular diseases refer to other cardiovascular diseases except hypertension, such as coronary heart disease, valvular heart disease and so on. The predictive outcome of our study was whether patients developed POAF following thoracoscopic lung cancer surgery during hospitalization. The atrial fibrillation in this study was diagnosed by clinicians based on the patient’s symptoms and electrocardiogram results and recorded in the medical records. It is worth noting that patients with a history of atrial fibrillation or those who develop atrial fibrillation before the operation do not belong to POAF.

Statistical analysis

Since we collected all eligible patients during the observation period, sample size estimation is not applicable. This study used the R language (version 4.4.2) and its related statistical software packages to conduct statistical analyses. For continuous variables that conform to the normal distribution, the statistical description was carried out using the mean ± standard deviation, and the independent samples t-test was used to compare the differences between groups. For continuous variables that do not conform to the normal distribution, the median and interquartile range were used for description, and the Wilcoxon rank-sum test was selected to conduct the comparison between the two groups. Categorical variables were expressed using frequencies and percentages. According to the specific distribution of the samples, the χ² test or Fisher’s exact test was selected to analyze the differences between groups. In this study, the bootstrap method was used to internally validate the prediction model to evaluate the performance stability of the model in similar samples and reduce the estimation bias caused by the limited sample size. The least absolute shrinkage and selection operator (LASSO) expression was used to screen the predictors of POAF (20). When determining the final candidate variables, the lambda value corresponding to the standard error was selected to ensure that the variables with representativeness and predictive value were screened out. Based on the finally screened candidate variables, a backward method was adopted to construct a multiple logistic regression model. At the same time, the variance inflation factor (VIF) was calculated to evaluate the multicollinearity in the model, ensuring the stability and reliability of the model. The final statistically significant risk factors were determined, and their odds ratios (OR) and 95% confidence intervals (CI) were calculated. When P<0.05, the difference was considered statistically significant. The “regplot” package in the R language was used to draw a nomogram, providing an intuitive visual display for the probability diagnostic prediction model of POAF for elderly patients with lung cancer. The “pROC” package was used to draw the receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC) to evaluate the discrimination ability of the nomogram model. The “calibrate” package was used to evaluate the calibration degree of the model to ensure the consistency between the model prediction results and the actual observation results. The “rmda” package was used to draw the decision curve analysis (DCA) diagram, and the clinical application value of the model was quantified through the net benefit, providing a reference for clinical decision-making (21).

Results

Participant characteristics

A total of 960 elderly patients with lung cancer were finally included in the study, of whom 83 (8.64%) were diagnosed with POAF. Table 1 compares the differences in characteristics between the POAF group and the non-POAF group. Compared with the non-POAF group, the POAF group showed significant differences in multiple aspects. In terms of age, the average age of the POAF group was higher. From the perspective of medical history, the prevalence rates of respiratory illness and non-hypertensive cardiovascular diseases among the patients in the POAF group were significantly higher. Regarding the surgery-related indicators, the implementation rates of lobectomy and lymph node dissection were higher, and the intraoperative blood loss was also greater. In terms of postoperative condition, the proportion of patients who received anti-infection treatment and required rescue analgesia three or more times in the POAF group was significantly higher than that in the non-POAF group, and the number of days for indwelling the thoracic drainage tube was also significantly prolonged.

Variable selection

A total of 26 variables were included in the ten-fold cross-validation of the LASSO regression (Figure 2A). When the cross-validation error was one standard error, 11 variables remained, namely age, high school and above, type 2 diabetes, respiratory illness, non-hypertensive cardiovascular diseases, pulmonary lobectomy, anti-infection treatment, lymph node dissection, drainage duration, critical value of hypokalemia, and electrolyte imbalance on the third postoperative day, as indicated by the vertical dashed line in Figure 2B.

Figure 2 Lasso variable filtering diagram. (A) Tuning parameter (λ) selection using LASSO penalized logistic regression with 10-fold cross-validation. (B) LASSO coefficient profiles of the radiomic features. LASSO, least absolute shrinkage and selection operator.

Univariate and multivariate logistic regression analysis

Subsequently, these 11 variables were included in the univariate regression analysis. The analysis results showed that there were statistically significant differences between the two groups in 8 variables, namely age, respiratory illness, non-hypertensive cardiovascular diseases, pulmonary lobectomy, anti-infection treatment, lymph node dissection, drainage duration and electrolyte imbalance on the third postoperative day (P<0.05).

Furthermore, these 8 variables were incorporated into the multivariate regression analysis using the backward strategy. According to the criterion of balancing model fit and complexity, it was finally found that age (OR 1.086; 95% CI: 1.041–1.132), respiratory illness (OR 2.545; 95% CI: 1.234–5.247), non-hypertensive cardiovascular diseases (OR 2.212; 95% CI: 1.300–3.764), pulmonary lobectomy (OR 1.776; 95% CI: 1.047–3.013), anti-infection treatment (OR 1.657; 95% CI: 0.963–2.850), lymph node dissection (OR 2.181; 95% CI: 1.241–3.833) and drainage duration (OR 1.083; 95% CI: 1.010–1.161) were independent risk factors for POAF in elderly patients with lung cancer (Table 2). Although the P value for anti-infection treatment was 0.07 in the multivariate analysis, the removal of this factor decreased the Akaike information criterion (AIC) of the model, so we retained it.

Predictive model development

A nomogram for predicting POAF among elderly patients undergoing lung cancer surgery was then drawn based on these 7 variables (Figure 3). A 78-year-old patient with lung cancer and non-hypertensive cardiovascular diseases, with no history of respiratory illness, was hypothesized to undergo a non-lobectomy surgery without lymph node dissection, and was left with a chest drain for 5 days postoperatively and treated with anti-infective therapy. Based on this information, it is deduced that the total score of this patient in the nomogram is 198, and the probability of developing POAF is approximately 18%. For the convenience of clinical use, we have developed a web-based calculator for predicting the probability of POAF based on this nomogram.

Figure 3 The nomogram for predicting POAF among elderly patients undergoing lung cancer surgery. *, P<0.05; **, P<0.01; ***, P<0.001. POAF, postoperative atrial fibrillation.

Predictive model’s discrimination and calibration

In this study, the AUC value was 0.782, and the 95% CI was 0.734–0.830. The C-index after 500 bootstrap resampling internal validations was 0.782, and the 95% CI was 0.734–0.835. According to the Youden index, the optimal cut-off value was 0.074, and the sensitivity and specificity were 0.660 and 0.807, respectively (Figure 4A). The calibration curve of the model showed a good match between the predicted values and the actual values, and its Brier value was 0.072 (Figure 4B). In addition, as another reference index for the model’s consistency, the Chi-square value of the Hosmer-Lemeshow goodness-of-fit test (HL test) of the model was 8.395, and the P value was 0.50.

Figure 4 Prediction of the model’s discrimination and calibration. (A) Receiver operating characteristic curve of the nomogram. (B) Calibration curve of the predictive POAF risk nomogram. The y-axis represents actual diagnosed cases of POAF, and the x-axis represents the predicted risk of POAF. The diagonal dotted line represents a perfect prediction by an ideal model, and the solid line represents the performance of the nomogram, with the results indicating that a closer fit to the diagonal dotted line represents a better prediction. AUC, the area under the curve; C (ROC), area under the ROC curve; D, deviance; Dxy, size of correlation between predicted and actual values; E90, 90% quantile of the difference between the predicted value and the actual value; E_avg, the average difference between the predicted value and actual value; E_max, the maximum absolute difference between the predicted value and the actual value; POAF, postoperative atrial fibrillation; Q, quality statistic; ROC, receiver operating characteristic; S:p, P value of the Z-test; S:z, Z-value of the Z-test; U, unreliability statistic.

Predictive model’s clinical applicability

Based on the predictive dataset for the occurrence of POAF, a clinical DCA was constructed (Figure 5). The horizontal axis represents the high-risk threshold, with a value range from 0.0 to 1.0, reflecting the situations under different risk judgment criteria; the vertical axis represents the standardized net benefit, also ranging from 0.0 to 1.0, which is used to measure the benefit levels under different decisions.

Figure 5 The DCA graph. The x-axis presents the intervention thresholds, and the y-axis represents the net benefit corresponding to a certain threshold. DCA, decision curve analysis.

Each line in the Figure 5 represents different factors or models: the red line represents the “Predicted nomogram”; the light pink line represents “Age”; the pale green line represents “Respiratory illness”; the light blue line represents “Non-hypertensive cardiovascular diseases”; the light cyan line represents “Pulmonary lobectomy”; the misty rose line represents “Anti-infection treatment”; the lavender line represents “Lymph node dissection”; the light gray line represents “Drainage duration”; the gray line represents “All”, showing the situation of the standardized net benefit when all factors are considered; the black line at the bottom represents “None”, serving as a reference baseline.

Figure 5 shows that as the high-risk threshold gradually increases, the standardized net benefits corresponding to each curve all exhibit a downward trend. Compared with the curves based on individual independent risk factors, the curve based on the predicted nomogram is located in the middle area of the diagram, being the farthest from the two extremes of “None” and “All”, indicating that the predicted nomogram has good clinical effectiveness and can provide a reliable basis for clinical decision-making.

Discussion

Current status analysis of POAF in elderly patients with lung cancer

With an increasingly aging society, clinicians now encounter more older patients in various surgical settings (22). Previous studies pointed out that among lung cancer patients, elderly patients have a higher probability of developing POAF compared to middle-aged and young patients. Moreover, the peak incidence of POAF in elderly lung cancer patients occurs within 72 hours after surgery (23). In this study, the incidence of POAF in elderly patients with lung cancer was 8.64%, which is higher than that reported in a previous study (24). This discrepancy may be related to differences in sample size, sample sources, and therapeutic protocols. Accurate identification of high-risk patients for POAF following lung cancer surgery is crucial for reducing postoperative complications, optimizing the utilization of medical resources, and improving patient outcomes (25). However, research on POAF in elderly patients with lung cancer remains relatively limited. Han et al. (24) conducted a retrospective study on POAF in elderly patients with lung cancer, but the duration of postoperative electrocardiographic monitoring was only 24 hours, which failed to capture the occurrence of POAF over a longer period. Additionally, the AUC value of the predictive model in their study was below 70%, indicating suboptimal predictive performance. Thus, we collected data on basic information, living habits, medical history, surgical-related information, and postoperative conditions in elderly patients with lung cancer. Based on these data, a nomogram was constructed using logistic regression to achieve a precise prediction of POAF in these patients.

Age is a dominant high-risk factor for POAF in elderly patients undergoing lung cancer surgery

Even among elderly patients undergoing lung cancer surgery, the incidence of POAF increases with age. The results of this study showed that the risk of POAF development increased by 1.086 times for every 1-year increase in age in elderly lung cancer patients. Similarly, a study conducted by Goulden et al. indicates that age is a preoperative influencing factor for patients undergoing cardiac surgery (26). Another study illustrated that advanced age is a significant contributor to the development of POAF after non-cardiac surgery (17). Elderly patients are more prone to POAF, probably due to their higher comorbidities, more significant release of postoperative inflammatory factors, increased surgical traumatic stress, and decreased cardiac function. This suggests that we should focus on the occurrence of POAF in elderly patients.

The previous history of non-hypertensive cardiovascular diseases is a high-risk factor for POAF in elderly patients undergoing lung cancer surgery

Given the high prevalence of hypertension in elderly patients, we specifically distinguished between hypertension and non-hypertensive cardiovascular diseases when collecting medical histories. Our study results indicated that POAF after lung cancer surgery was not associated with hypertension but was significantly related to non-hypertensive cardiovascular diseases such as valvular heart disease and coronary heart disease. Specifically, the risk of developing POAF was 2.618 times higher in patients with non-hypertensive cardiovascular diseases compared to those without, a finding that aligns with the study by Tong et al. (25). The exact pathways and physiological mechanisms underlying the development of POAF remain incompletely understood (27). However, it is noted that POAF is also a common complication for cardiac surgeries such as coronary artery bypass grafting (CABG) and valve replacement surgery (28,29). Thus, it is hypothesized that since atrial fibrillation itself is a cardiovascular disease, patients with underlying non-hypertensive cardiovascular conditions may have pre-existing structural and functional abnormalities in the heart. These abnormalities are likely to be exacerbated by the physiological stress of surgery. Additionally, certain cardiovascular diseases may weaken cardiac reserve function, thereby reducing the heart's capacity to tolerate perioperative stress.

A history of respiratory illness increases the risk of POAF in elderly patients undergoing lung cancer surgery

A history of respiratory illness, including chronic obstructive pulmonary disease (COPD), emphysema, bronchiectasis, bronchitis and pulmonary infection, is strongly associated with an elevated risk of POAF in elderly patients undergoing lung cancer surgery. An observational study of POAF in patients receiving CABG suggested that COPD was a predictor of POAF in CABG (30). Results of another study on POAF among patients undergoing hip fracture surgery showed that COPD was also a predictor of POAF in hip fracture surgery (31). In this study, there were 58 patients (6.04%) with respiratory disease, and the probability of POAF in elderly lung cancer patients with respiratory disease was 2.365 times higher than those without respiratory disease. The compromised pulmonary function in these patients may lead to hypoxia, hypercapnia, and increased pulmonary vascular resistance, all of which can trigger POAF. Furthermore, patients with respiratory disease generally have weaker lung function, so there is a greater likelihood of postoperative pulmonary complications and a relatively longer chest tube retention time, and all of these factors are associated with POAF.

Pulmonary lobectomy is a risk factor for POAF in elderly patients undergoing lung cancer surgery

Pulmonary lobectomy, a common surgical procedure for lung cancer, is independently associated with an increased risk of POAF in elderly lung cancer patients. The extensive tissue resection and associated inflammatory response may contribute to atrial irritability and arrhythmogenesis. Compared to lung wedge resection or segmental resection, lobectomy is more traumatic and has a more intense inflammatory response, increasing the incidence of POAF. In this study, the total number of patients who underwent lobectomy was 292, of which 43 patients had a concomitant POAF, and the prevalence of POAF in patients with lobectomy was calculated to be 14.7%, which is slightly lower than the prevalence found in the results of the study by Ng et al. (18.8%) (32). Similarly, in cardiac surgery, POAF is more likely to occur in surgeries with extensive surgical resection, such as CABG and valve replacement (33,34). A study examining the relationship between the type of esophageal cancer surgery and POAF showed that minimally invasive esophageal resection resulted in a lower prevalence of POAF compared to open esophageal cancer resection (35). The above suggests that the extent of surgical resection is associated with POAF. Based on this finding, we suggest that less invasive lung resections be performed in elderly lung cancer patients when available to reduce the prevalence of POAF after lung cancer surgery.

Anti-infection treatment is associated with an increased risk of POAF in elderly patients who undergo lung cancer surgery

The administration of anti-infection treatments, such as broad-spectrum antibiotics, has been identified as a potential risk factor for POAF in elderly patients undergoing lung cancer surgery. The results of a study analyzing the relationship between anti-inflammatory drugs and POAF showed that anti-inflammatory strategies have an important role in preventing POAF (36). The implementation of postoperative anti-infection treatment indirectly reflects the presence of local or systemic inflammation in the patient, whereas interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) have been shown to induce POAF by affecting the electrophysiological properties of the atria or promoting atrial fibrosis (37). Previous studies have suggested that inflammation is associated with the mechanism of POAF development (38) and validation of related markers, such as C-reactive protein, is associated with the incidence of POAF (39). The results of this study showed that the risk of POAF in elderly lung cancer patients who underwent anti-infection treatment was 1.886 times higher than that in those who did not undergo anti-infection treatment, which is consistent with the above studies. In the future, anti-inflammatory drugs such as statins or colchicine can be applied to prevent the occurrence of POAF (40).

Lymph node dissection elevates the risk of POAF in elderly patients undergoing lung cancer surgery

Lymph node dissection, a routine component of lung cancer radical surgery, is a significant risk factor for POAF in elderly lung cancer patients. A study investigating the effect of lymph node dissection on postoperative complications in patients undergoing surgery for NSCLC showed that the incidence of POAF was higher in patients with lymph node dissection than in those without lymph node dissection, which is consistent with the results of this study (41). Similarly, there was a statistical difference in the percentage of lymph node dissection between esophagectomy patients who developed POAF and those who did not (42). POAF may be triggered by lymph node dissection due to injury to autonomic nerves located in the periportal or mediastinal lymph node dissection, which further leads to denervation of the cardiac autonomic system (43).

Drainage duration is a critical predictor of POAF in elderly patients undergoing lung cancer surgery

Prolonged chest tube drainage duration is a notable risk factor for POAF in elderly patients following lung cancer surgery. The persistent presence of a foreign body in the thoracic cavity may lead to ongoing inflammation and irritation, increasing the likelihood of POAF. The results of Ng et al.’s study showed that the mean drainage duration was longer in patients in the POAF group compared to the non-POAF group with the same surgical approach, which is consistent with the findings of this paper (32). Therefore, we suggest choosing a thinner and softer catheter to reduce the local irritation and pain level, as well as shortening the length of catheter retention, thus reducing the incidence of POAF.

The clinical value of the predictive model for POAF in elderly lung cancer patients

The nomogram, as a visualization of logistic regression, presents the results of logistic regression intuitively and clearly (44). This study is the first to construct a predictive model for POAF after lung cancer surgery in the elderly. Although POAF, as a type of secondary atrial fibrillation, can generally revert to normal sinus rhythm on its own, a previous study has shown that untreated POAF after thoracic and cardiac surgery may potentially progress to severe stroke, or even death (45). The surgical environment acts like a stress test. Patients who develop POAF are more likely to experience atrial fibrillation in the future (46). Clinical medical staff can use the nomogram predictive model constructed in this study to screen high-risk groups for POAF after lung cancer surgery in the elderly. To translate the nomogram into clinical practice, we propose risk-stratified monitoring: high-risk patients should receive extended electrocardiogram monitoring and prophylactic amiodarone. For POAF lasting >12 hours, early direct oral anticoagulant initiation should be considered unless contraindicated. These strategies align with ESC guidelines emphasizing individualized atrial fibrillation management in surgical patients (47).

Limitations

Several limitations are in our research. First, this study is a single-center investigation and has not undergone external validation; therefore, the generalizability of the model remains unknown. Additionally, this study is retrospective in design, with all variables sourced from the electronic medical record system. The model did not incorporate factors such as genetic markers, preoperative echocardiography examination, or non-routine laboratory tests like IL-6. Third, data on severe complications in the POAF group have not been collected. In the future, it will be necessary to consider a broader range of potential influencing factors and conduct multicenter prospective studies to further optimize the model’s predictive performance and clinical applicability.

Conclusions

We developed a diagnostic nomogram by integrating seven indicators to assess POAF risk for geriatric patients receiving thoracoscopic lung cancer surgery. The model has good identifiability, accuracy, and clinical practicability. The model can provide the basis for early identification of high-risk patients, help optimize the allocation of resources, and improve China’s older interventions in patients undergoing thoracoscopic lung cancer surgery.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-533/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-533/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-533/prf

Funding: This work was supported by the Medical Science and Technology Project of Zhejiang Provincial Health Commission (grant No. 2023KY1299).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-533/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Zhoushan Hospital (No. 2022-155). The requirement to obtain informed consent from patients was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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